# Copyright 2020 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Benchmarks on Hierarchical RNN on MNIST digits."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import tensorflow.compat.v2 as tf

from keras.benchmarks import benchmark_util


class HierarchicalRNNBenchmark(tf.test.Benchmark):
    """Benchmarks for Hierarchical RNN using `tf.test.Benchmark`."""

    def __init__(self):
        super().__init__()
        self.num_classes = 10
        self.row_hidden, self.col_hidden = 128, 128
        (self.x_train, self.y_train), _ = tf.keras.datasets.mnist.load_data()
        self.x_train = self.x_train.reshape(self.x_train.shape[0], 28, 28, 1)
        self.x_train = self.x_train.astype("float32") / 255
        self.y_train = tf.keras.utils.to_categorical(
            self.y_train, self.num_classes
        )

    def _build_model(self):
        """Model from https://github.com/keras-team/keras/blob/master/examples

        /mnist_hierarchical_rnn.py.
        """
        row, col, pixel = self.x_train.shape[1:]
        inputs = tf.keras.layers.Input(shape=(row, col, pixel))
        encoded_rows = tf.keras.layers.TimeDistributed(
            tf.keras.layers.LSTM(self.row_hidden)
        )(inputs)
        encoded_cols = tf.keras.layers.LSTM(self.col_hidden)(encoded_rows)
        outputs = tf.keras.layers.Dense(self.num_classes, activation="softmax")(
            encoded_cols
        )
        model = tf.keras.Model(inputs, outputs)

        return model

    # In each benchmark test, the required arguments for the
    # method `measure_performance` include:
    #   x: Input data, it could be Numpy or loaded from tfds.
    #   y: Target data. If `x` is a dataset or generator instance,
    #      `y` should not be specified.
    #   loss: Loss function for model.
    #   optimizer: Optimizer for model.
    #   Check more details in `measure_performance()` method of
    #   benchmark_util.
    def benchmark_hrnn_mnist_bs_256(self):
        """Measure performance with batch_size=256."""
        batch_size = 256
        metrics, wall_time, extras = benchmark_util.measure_performance(
            self._build_model,
            x=self.x_train,
            y=self.y_train,
            batch_size=batch_size,
            optimizer="rmsprop",
            loss="categorical_crossentropy",
            metrics=["accuracy"],
        )

        metadata = benchmark_util.get_keras_examples_metadata(
            "hierarchical_rnn", batch_size
        )
        extras.update(metadata)
        self.report_benchmark(
            wall_time=wall_time, metrics=metrics, extras=extras
        )

    def benchmark_hrnn_mnist_bs_512(self):
        """Measure performance with batch_size=512."""
        batch_size = 512
        metrics, wall_time, extras = benchmark_util.measure_performance(
            self._build_model,
            x=self.x_train,
            y=self.y_train,
            batch_size=batch_size,
            optimizer="rmsprop",
            loss="categorical_crossentropy",
            metrics=["accuracy"],
        )

        metadata = benchmark_util.get_keras_examples_metadata(
            "hierarchical_rnn", batch_size
        )
        extras.update(metadata)
        self.report_benchmark(
            wall_time=wall_time, metrics=metrics, extras=extras
        )

    def benchmark_hrnn_mnist_bs_1024(self):
        """Measure performance with batch_size=1024."""
        batch_size = 1024
        metrics, wall_time, extras = benchmark_util.measure_performance(
            self._build_model,
            x=self.x_train,
            y=self.y_train,
            batch_size=batch_size,
            optimizer="rmsprop",
            loss="categorical_crossentropy",
            metrics=["accuracy"],
        )

        metadata = benchmark_util.get_keras_examples_metadata(
            "hierarchical_rnn", batch_size
        )
        extras.update(metadata)
        self.report_benchmark(
            wall_time=wall_time, metrics=metrics, extras=extras
        )

    def benchmark_hrnn_mnist_bs_1024_gpu_2(self):
        """Measure performance with batch_size=1024, gpu=2 and

        distribution_strategy='mirrored'
        """
        batch_size = 1024
        metrics, wall_time, extras = benchmark_util.measure_performance(
            self._build_model,
            x=self.x_train,
            y=self.y_train,
            batch_size=batch_size,
            num_gpus=2,
            distribution_strategy="mirrored",
            optimizer="rmsprop",
            loss="categorical_crossentropy",
            metrics=["accuracy"],
        )

        metadata = benchmark_util.get_keras_examples_metadata(
            "hierarchical_rnn", batch_size
        )
        extras.update(metadata)
        self.report_benchmark(
            wall_time=wall_time, metrics=metrics, extras=extras
        )


if __name__ == "__main__":
    tf.test.main()
